Core Ideas: OZCAR is a network of sites studying the critical zone. OZCAR covers various disciplines. OZCAR will help disciplines to work together for a better representation and modeling of the critical zone. The French critical zone initiative, called OZCAR (Observatoires de la Zone Critique–Application et Recherche or Critical Zone Observatories–Application and Research) is a National Research Infrastructure (RI). OZCAR‐RI is a network of instrumented sites, bringing together 21 pre‐existing research observatories monitoring different compartments of the zone situated between “the rock and the sky,” the Earth's skin or critical zone (CZ), over the long term. These observatories are regionally based and have specific initial scientific questions, monitoring strategies, databases, and modeling activities. The diversity of OZCAR‐RI observatories and sites is well representative of the heterogeneity of the CZ and of the scientific communities studying it. Despite this diversity, all OZCAR‐RI sites share a main overarching mandate, which is to monitor, understand, and predict (“earthcast”) the fluxes of water and matter of the Earth's near surface and how they will change in response to the “new climatic regime.” The vision for OZCAR strategic development aims at designing an open infrastructure, building a national CZ community able to share a systemic representation of the CZ, and educating a new generation of scientists more apt to tackle the wicked problem of the Anthropocene. OZCAR articulates around: (i) a set of common scientific questions and cross‐cutting scientific activities using the wealth of OZCAR‐RI observatories, (ii) an ambitious instrumental development program, and (iii) a better interaction between data and models to integrate the different time and spatial scales. Internationally, OZCAR‐RI aims at strengthening the CZ community by providing a model of organization for pre‐existing observatories and by offering CZ instrumented sites. OZCAR is one of two French mirrors of the European Strategy Forum on Research Infrastructure (eLTER‐ESFRI) project.

Impact of GPS (Global Positioning System) data assimilation is assessed here using a high-resolution numerical weather prediction system at 2.5 km horizontal resolution. The Zenithal Tropospheric Delay (ZTD) GPS data from mesoscale networks are assimilated with the 3DVAR AROME data assimilation scheme. Data from more than 280 stations over the model domain have been assimilated during 15-day long assimilation cycles prior each of the two studied events. The results of these assimilation cycles show that the assimilation of GPS ZTD with the AROME system performs well in producing analyses closer to the ZTD observations in average. Then the impacts of assimilating GPS data on the precipitation forecast have been evaluated. For the first case, only the AROME runs starting a few hours prior the triggering of the convective system are able to simulate the convective precipitation. The assimilation of GPS ZTD observations improves the simulation of the spatial extent of the precipitation, but slightly underestimates the heaviest precipitation in that case compared with the experiment without GPS. The accuracy of the precipitation forecast for the second case is much better. The analyses from the control assimilation cycle provide already a good description of the atmosphere state that cannot be further improved by the assimilation of GPS observations. Only for the latest day (22 November 2007), significant differences have been found between the two parallel cycles. In that case, the assimilation of GPS ZTD allows to improve the first 6 to 12 h of the precipitation forecast.

An intensive water vapour intercomparison effort, involving airborne and ground‐based water vapour lidar systems, was carried out in the framework of the COPS experiment. The main objective of this paper is to provide accurate error estimates for these systems.Comparisons between the ground‐based Raman lidar BASIL and the airborne CNRS DIAL (Differential Absorption Lidar) indicate a mean relative bias between the two sensors, calculated with respect to the mean value of −2.13% (−0.034 g kg⁻¹) in the altitude region 0.5–3.5 km, while comparisons between BASIL and the airborne DLR DIAL lead to a mean relative bias of 1.87% (0.018 g kg⁻¹) in this same altitude region.Comparisons between the ground‐based UHOH DIAL and the CNRS DIAL indicate a bias of −3.2% (−0.37 × 10²² m⁻³) in the altitude range 1.5–4.5 km, while comparisons between the UHOH DIAL and the DLR DIAL indicate a bias of 0.83% (0.06 × 10²² m⁻³) in this same altitude range. Based on the available comparisons between the ground‐based Raman lidar BERTHA and the CNRS DIAL, the mean relative bias is found to be −4.37% (−0.123 g kg⁻¹) in the altitude region 0.5–4.5 km. Comparisons between the ground‐based IGN Raman lidar and the CNRS DIAL indicate a bias of 3.18% (0.55 g kg⁻¹) in the altitude range from 0.5 to 4.5 km, while comparisons between the CNRS DIAL and DLR DIAL result in a mean relative bias of 3.93% (1.1 × 10²² m⁻³) in the altitude interval 0.5–4.0 km. Based on the available statistics of comparisons, benefiting from the fact that the CNRS DIAL was able to be compared with all other lidar systems, and putting equal weight on the data reliability of each instrument, overall relative values for BASIL, BERTHA, IGN Raman lidar, UHOH DIAL, DLR DIAL, and CNRS DIAL, with respect to the mean value, are found to be −0.38, −2.60, 4.90, −1.43, −2.23 and 1.72%, respectively. Copyright © 2011 Royal Meteorological Society